Implementations of classical and machine learning models for survival analysis, including deep neural networks via 'keras' and 'tensorflow'. Each model includes a separated fit and predict interface with consistent prediction types for predicting risk or survival probabilities. Models are either implemented from 'Python' via 'reticulate' <https://CRAN.R-project.org/package=reticulate>, from code in GitHub packages, or novel implementations using 'Rcpp' <https://CRAN.R-project.org/package=Rcpp>. Neural networks are implemented from the 'Python' package 'pycox' <https://github.com/havakv/pycox>.
Version: 0.1.191 Imports: Rcpp (≥ 1.0.5) LinkingTo: Rcpp Suggests: keras (≥ 2.11.0), pseudo, reticulate, survival Published: 2024-03-19 DOI: 10.32614/CRAN.package.survivalmodels Author: Raphael Sonabend [aut], Yohann Foucher [cre] Maintainer: Yohann Foucher <yohann.foucher at univ-poitiers.fr> BugReports: https://github.com/foucher-y/survivalmodels/issues License: MIT + file LICENSE URL: https://github.com/RaphaelS1/survivalmodels/ NeedsCompilation: yes Materials: README CRAN checks: survivalmodels results Documentation: Reference manual: survivalmodels.pdf Downloads: Package source: survivalmodels_0.1.191.tar.gz Windows binaries: r-devel: survivalmodels_0.1.191.zip, r-release: survivalmodels_0.1.191.zip, r-oldrel: survivalmodels_0.1.191.zip macOS binaries: r-release (arm64): survivalmodels_0.1.191.tgz, r-oldrel (arm64): survivalmodels_0.1.191.tgz, r-release (x86_64): survivalmodels_0.1.191.tgz, r-oldrel (x86_64): survivalmodels_0.1.191.tgz Old sources: survivalmodels archive Linking:Please use the canonical form https://CRAN.R-project.org/package=survivalmodels to link to this page.
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